Certain Member States of the European Union Do Not Seem Eager to Adopt the Euro. Why? An Example of Arguments vs. Facts

Economic analysis may be threatened by politics and there has been plenty of politics in regard to the euro. Certain Central and Eastern European countries, after they became members of the European Union in 2004, started the process to join the euro area. There seemed to be broad political consensus and enthusiasm for the common currency and the European Monetary Union in those countries at the time. Prior to the global financial crisis that started in 2008, institutional research and academic and other arguments for adopting the euro focused on cost-benefit analyses emphasizing positive effects of the euro. Twenty years after the introduction of the euro, certain EU member states do not seem enthusiastic to give up their national currency. The key reason seems to be that the financial crisis revealed the incomplete monetary architecture of the euro area. This research reviews key arguments for the adoption of the euro before the crisis and compares them to the evidence before and after the crisis. The analytical framework used includes an example of a country with the euro (Greece) in comparison with its two neighboring countries without the euro (Bulgaria and Romania) in the region of southeastern Europe and the Western Balkans. The analysis finds that good times benefit all, while bad times can bring disproportionate harm to the country with the euro. Keywords: monetary policy, euro, southeastern Europe, European Monetary Unio

Towards a Theory of Complexity Matching

Today there is still much debate regarding the definition of a complex system and complexity. We limit ourselves to two-state systems which jump randomly from one state to another and thus give rise to a dichotomous stochastic process. Our definition of a complex system is based on two properties: power-law statistics and renewal. The former implies that the waiting time distribution for both states is an inverse-power law with a finite exponent. The latter is a property whereby the time of permanence in one state is completely independent from the time of permanence in the other. If the system obeys power-law statistics with an exponent smaller than 2, then it is always complex. If, on the other hand, the exponent is greater than 2, then the system is complex only in the non-stationary stage. Recently, it has been shown that the linear response of a complex system to a coherent perturbation vanishes in the long-time limit. This result, together with the hypothesis that complex systems can be excited only by other complex systems, is the key motivation for the work presented in this thesis. It is part of an ongoing search for a theory of complexity matching, a theory showing that complex systems respond only if they are excited by other complex systems and that otherwise the response is attenuated. In the first part of the thesis we explore the possibility of coupling two Poisson processes. Our approach is based on the experience obtained in the field of stochastic resonance. We try to perturb a system that obeys ordinary Poisson statistics using Poissonian signals with different rates. To this end, we adopt a simple model that reproduces aperiodic stochastic resonance and we show that such a phenomenon is not present in the more general case in which the rate is not necessarily induced by some kind of noise. Furthermore, we adopt the concept of events and use it to study the interaction of Poisson systems. We discover that, when the system produces events at a lower rate with respect to the perturbation, the events of the perturbation become attractors of the system events and vice versa. We use the term rate matching to identify the condition when the two rates of event production are the same. The second part of the thesis deals with signals produced by complex systems. In order to present the full theory of complexity matching, a new fluctuation-dissipation theorem must be introduced, but this goes beyond the scope of the work presented here. However, the understanding of the non-ergodic nature of complex systems is fundamental to the application of the new fluctuation dissipation theorem. Therefore, here we study the power-spectrum of complex signals and show that 1/f-noise is produced by systems that lie on the border that separates ergodic systems from non-ergodic ones. We do this by generalising the Wiener-Khinchin theorem and extending it to non-stationary non-ergodic processes. We distinguish between two different types of truncation effects: the physical truncation, where we use a truncated waiting time distribution, and an observation-induced effect, which is a consequence of finite acquisition times. It is the finite observation time that allows us to apply the generalised Wiener-Khinchin theorem in the non-ergodic case. Our final results show that the power-spectrum is related to the frequency via an inverse power law and that, in the non-ergodic condition, the power-spectrum also depends on the observation time

Impact of embedding on predictability of failure-recovery dynamics in networks

Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network's components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component.We identify a metastable domain in the global network phase diagram spanned by the model's control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. The loss of predictability due to these effects depend on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand predictability and controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in the global network.Comment: 22 pages, 20 figure

A comprehensive approach to MPSoC security: achieving network-on-chip security : a hierarchical, multi-agent approach

Multiprocessor Systems-on-Chip (MPSoCs) are pervading our lives, acquiring ever increasing relevance in a large number of applications, including even safety-critical ones. MPSoCs, are becoming increasingly complex and heterogeneous; the Networks on Chip (NoC paradigm has been introduced to support scalable on-chip communication, and (in some cases) even with reconfigurability support. The increased complexity as well as the networking approach in turn make security aspects more critical. In this work we propose and implement a hierarchical multi-agent approach providing solutions to secure NoC based MPSoCs at different levels of design. We develop a flexible, scalable and modular structure that integrates protection of different elements in the MPSoC (e.g. memory, processors) from different attack scenarios. Rather than focusing on protection strategies specifically devised for an individual attack or a particular core, this work aims at providing a comprehensive, system-level protection strategy: this constitutes its main methodological contribution. We prove feasibility of the concepts via prototype realization in FPGA technology

The influence of statistical properties of Fourier coefficients on random surfaces

Many examples of natural systems can be described by random Gaussian surfaces. Much can be learned by analyzing the Fourier expansion of the surfaces, from which it is possible to determine the corresponding Hurst exponent and consequently establish the presence of scale invariance. We show that this symmetry is not affected by the distribution of the modulus of the Fourier coefficients. Furthermore, we investigate the role of the Fourier phases of random surfaces. In particular, we show how the surface is affected by a non-uniform distribution of phases

Deep learning for time series forecasting: The electric load case

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short-term forecast (one-day-ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence-to-sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one